DESCRIPTION (provided by investigator): Neighborhood social, economic, demographic, and built characteristics have been shown to influence health outcomes such as mortality, obesity, cardiovascular disease, and depression, independent of individual factors. Recent literature suggests that the neighborhood socioeconomic status (NSES) of the area in which individuals live is correlated with cognitive functioning, independent of individual factors. However, no studies to date have examined longitudinal relationships in a nationally representative sample. In addition, studies have focused on NSES without examination of other objective health-relevant neighborhood characteristics and perceived neighborhood characteristics, have not considered other sensitive late-life cognitive outcomes such as cognitive impairment, and have not addressed whether selection into neighborhoods, based on health status for example, may bias results Objective. This project will fill gaps in existing research by developing a national database of objective neighborhood characteristics, linking these data with longitudinal data on cognitive function assessed in a nationally representative sample of older adults in the Health and Retirement Study (HRS) assessed over 22 years (1992-2014), and examining the relationship between both objective and perceived neighborhood characteristics and cognitive function over time. This study will also examine a host of potential mediators including biomarkers of stress, and vascular, behavioral, and psychosocial factors that may inform mechanistic pathways by which neighborhoods influence cognitive outcomes. It will also identify subgroups that are most vulnerable to neighborhood effects based on sociodemographic and genetic characteristics, and test the sensitivity of our findings to selection bias Methods. We will develop a database of objective neighborhood measures including NSES, racial/ethnic segregation and composition, urban sprawl, and food environment, and integrate it with individual data on cognitive function and subjective ratings of neighborhoods from the HRS and with Medicare data on clinical diagnoses of vascular events. Structural equation models will examine independent associations, mediation, and effect modification of neighborhood effects. An innovative statistical technique called omitted variable analysis will address the potential that individuals may selectively move into differing neighborhoods by health status and propensity score matching will be used to reduce potential biases in identifying the effects of neighborhood characteristics on cognitive outcomes Implications. This research can shed light on contextual characteristics that may serve as modifiable targets for interventions, and yield larger-scale changes in the effort to prevent cognitive decline and impairment than interventions that focus on individual factors. Mediation analyses will identify underlying biological, vascular, behavioral, and psychosocial mechanisms to pinpoint more proximal intervention targets to buffer against the effects of living in a lower quality neighborhood. Identification of vulnerable subgroups will focus public health interventions more efficiently on at-risk groups.